{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 20, 1, 64, 64)\n",
      "float64\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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6tBecbeu9xnFeesaupFdcJ65LZ7fXB6rF0JAFRd9EU0h6xr7qtmYqNTqO1V02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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d442029d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nDca20XMi3rlqIVIZcfQy7tOJHj8Lx95oMF1ek0nRmPn8bc1itjw3l+KRr1Hw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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d9a265f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "#% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "# from IPython import get_ipython\n",
    "# get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "b = np.load('/usr/not-backed-up/unsupervised-videos-master/bouncing_mnist_test.npy')\n",
    "# b = np.divide(b,float(255))\n",
    "b = np.reshape(b,[10000,20,1,64,64])\n",
    "# b = np.float32(b)\n",
    "b = b.astype('float') / 255.\n",
    "print b.shape\n",
    "print b.dtype\n",
    "print b[1,1,:,:].max()\n",
    "data = np.zeros([20000,10,1,64,64])\n",
    "for i in range(20000):\n",
    "    data[2*i,:,:,:] = b[:,0:10,:,:,:]\n",
    "    data[2*i+1,:,:,:] = b[:,10:20,:,:,:]\n",
    "# a = b[0:100,0:10,:,:]\n",
    "# c = np.zeros(a.shape)\n",
    "# for i in range(10):\n",
    "#     c[:,i,:,:] = a[:,9-i,:,:]\n",
    "# plt.figure()\n",
    "# for i in range(10):\n",
    "# #     print(i)\n",
    "#     plt.subplot(2,5,i+1)\n",
    "#     plt.imshow(a[19,i,0,:,:])\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "# plt.figure()\n",
    "# for i in range(10):\n",
    "# #     print(i)\n",
    "#     plt.subplot(2,5,i+1)\n",
    "#     plt.imshow(c[19,i,0,:,:])\n",
    "# plt.show()\n",
    "h5f = h5py.File('/usr/not-backed-up/1_convlstm/bouncing_mnist_train.h5','w')\n",
    "h5f.create_dataset('input',shape = b[0:9000,0:10,:,:,:].shape, dtype=b.dtype)\n",
    "h5f.create_dataset('match',shape = b[0:9000,0:10,:,:,:].shape, dtype=b.dtype)\n",
    "h5f['input'][:] = b[0:9000,0:10,:,:,:]\n",
    "h5f['match'][:] = b[0:9000,10:20,:,:,:]\n",
    "h5f.close()\n",
    "\n",
    "\n",
    "# h5f = h5py.File('/usr/not-backed-up/1_convlstm/bouncing_mnist_test.h5','w')\n",
    "# h5f.create_dataset('input',shape = b[9000:10000,0:10,:,:,:].shape, dtype=b.dtype)\n",
    "# h5f.create_dataset('match',shape = b[9000:10000,0:10,:,:,:].shape, dtype=b.dtype)\n",
    "# h5f['input'][:] = b[9000:10000,0:10,:,:,:]\n",
    "# h5f['match'][:] = b[9000:10000,10:20,:,:,:]\n",
    "\n",
    "# h5f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 10, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "h5 = h5py.File('/usr/not-backed-up/1_convlstm/bouncing_mnist_test.h5','r')\n",
    "input_data = h5['input'][:]\n",
    "match = h5['match'][:]\n",
    "h5.close() \n",
    "print(input_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.amax(input_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(input_data[1,1,0,2,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yGhXBMt/ZlLb7S8oV9HCjUt/LsMKNSl+971fJaXQCpPMoQwgRnZQq6OFOZgRe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\nCGETUtCFEMImpKALIYRNSEEXQgib+P+U1R8sXr8JowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d99d1ff90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K/xvNKhegyRhTGu/3ipXB973F7+4rpw/BkzH7t3sB73XHyy6fRNH2HUyjHPAAsX//JFsm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d4473b7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "for i in range(10):\n",
    "#     print(i)\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.imshow(input_data[19,i,0,:,:])\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "for i in range(10):\n",
    "#     print(i)\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.imshow(match[19,i,0,:,:])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'sequence']\n",
      "[[ 0.  0.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]\n",
      " [ 1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "h5 = h5py.File('seq.h5','r')\n",
    "print(h5.keys())\n",
    "seq = h5['sequence'][:]\n",
    "print(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
